Image binarization converts an image's gray level to a black and white image and is an essential task in the fields of image processing and computer vision. Frequently, image binarization is performed in the preprocessing stage of different document image processing related applications such as optical character recognition (OCR) and document image retrieval. The simplest way to use image binarization is to choose a threshold value and classify all pixels with values above this threshold as white, and all other pixels as black. The problem then is how to select the correct threshold. In many cases, finding one threshold compatible to an entire image is very difficult, and in many cases even impossible. Therefore, adaptive image binarization is needed where an optimal threshold is chosen for each image area. Factors that make finding a thresholding difficult include ambient illumination, variance of gray levels within the object and the background and insufficient contrast. Degradations in images result from poor quality of paper, the printing process, ink blot and fading, document aging, extraneous marks, noise from scanning, etc. The goal of image binarization or more generally image categorization is to remove some of these artifacts and recover an image that is close to what one would obtain under ideal printing and imaging conditions.